Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests

Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests

A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the performed tests on data from four Swedish wind par...

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Bibliographic Details
Main Authors: Jennie Molinder, Sebastian Scher, Erik Nilsson, Heiner Körnich, Hans Bergström, Anna Sjöblom
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Energies
Subjects:
wind energy
icing on wind turbines
machine learning
probabilistic forecasting
Online Access:https://www.mdpi.com/1996-1073/14/1/158

Internet

https://www.mdpi.com/1996-1073/14/1/158

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